The paper presents a feature-rich approach to the automatic recognition and
categorization of named entities (persons, organizations, locations, and
miscellaneous) in news text for Bulgarian. We combine well-established features
used for other languages with language-specific lexical, syntactic and
morphological information. In particular, we make use of the rich tagset
annotation of the BulTreeBank (680 morpho-syntactic tags), from which we derive
suitable task-specific tagsets (local and nonlocal). We further add
domain-specific gazetteers and additional unlabeled data, achieving F1=89.4%,
which is comparable to the state-of-the-art results for English.

该论文提出了一种功能丰富的方法，用于对新闻文本中的命名实体（人物、组织、地点和其他）进行自动识别和分类，特别重视 Bulgarian 的词汇、句法和形态的信息以及本土化的标记集和领域特定的地名词典的使用，达到了与英语最先进结果相当的 89.4% 的 F1 值。

使用条件随机场进行保加利亚语特征丰富的命名实体识别

Feature-Rich Named Entity Recognition for Bulgarian Using Conditional  Random Fields

We present experiments with part-of-speech tagging for Bulgarian, a Slavic
language with rich inflectional and derivational morphology. Unlike most
previous work, which has used a small number of grammatical categories, we work
with 680 morpho-syntactic tags. We combine a large morphological lexicon with
prior linguistic knowledge and guided learning from a POS-annotated corpus,
achieving accuracy of 97.98%, which is a significant improvement over the
state-of-the-art for Bulgarian.

本文针对保加利亚语 (一种具有丰富的屈折和派生形态的斯拉夫语言) 进行了词性标注实验，并结合大型形态学词典、先前的语言知识以及基于 POS 注释语料库的有指导性学习方法，使用 680 个形态语法标记，实现了 97.98% 的准确率，这是对保加利亚语的最新技术的显着改进。

适用于形态复杂语言的特征丰富的词性标注：以保加利亚语为例

Feature-Rich Part-of-speech Tagging for Morphologically Complex  Languages: Application to Bulgarian

Recently, reading comprehension models achieved near-human performance on
large-scale datasets such as SQuAD, CoQA, MS Macro, RACE, etc. This is largely
due to the release of pre-trained contextualized representations such as BERT
and ELMo, which can be fine-tuned for the target task. Despite those advances
and the creation of more challenging datasets, most of the work is still done
for English. Here, we study the effectiveness of multilingual BERT fine-tuned
on large-scale English datasets for reading comprehension (e.g., for RACE), and
we apply it to Bulgarian multiple-choice reading comprehension. We propose a
new dataset containing 2,221 questions from matriculation exams for twelfth
grade in various subjects -history, biology, geography and philosophy-, and 412
additional questions from online quizzes in history. While the quiz authors
gave no relevant context, we incorporate knowledge from Wikipedia, retrieving
documents matching the combination of question + each answer option. Moreover,
we experiment with different indexing and pre-training strategies. The
evaluation results show accuracy of 42.23%, which is well above the baseline of
24.89%.

研究了在英文大规模数据集中预训练的多语言 BERT Fine-Tuning 在保加利亚语阅读理解中的效果，构建了一个新的包含历史、生物、地理和哲学等学科的 12 年级毕业考试和 412 个在线历史测验问题的数据集，并利用不同的索引和预训练策略进行实验，评估结果表明，准确率为 42.23％，大大超过了基线水平 24.89％。